research-article

Accelerating simulation of agent-based models on heterogeneous architectures

Authors:

Sudhakar YalamanchiliAuthors Info & Claims

GPGPU-6: Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units

Pages 108 - 119

https://doi.org/10.1145/2458523.2458534

Published: 16 March 2013 Publication History

Abstract

The wide usage of GPGPU programming models and compiler techniques enables the optimization of data-parallel programs on commodity GPUs. However, mapping GPGPU applications running on discrete parts to emerging integrated heterogeneous architectures such as the AMD Fusion APU and Intel Sandy/Ivy bridge with the CPU and the GPU on the same die has not been well studied.

Classic time-step simulation applications represented by agent-based models have the intrinsic parallel structure that is a good fit for GPGPU architectures. However, when mapping these applications directly to the integrated GPUs, the performance may degrade due to less computation units and lower clock speed.

This paper proposes an optimization to the GPGPU implementation of the agent-based model and illustrates it in the traffic simulation example. The optimization adapts the algorithm by moving part of the workload to the CPU to leverage the integrated architecture and the on-chip memory bus which is faster than the PCIe bus that connects the discrete GPU and the host. The experiments on discrete AMD Radeon GPU and AMD Fusion APU demonstrate that the optimization can achieve 1.08--2.71x performance speedup on the integrated architecture over the discrete platform.

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Cited By

Xiao JAndelfinger PEckhoff DCai WKnoll A(2019)A Survey on Agent-based Simulation Using Hardware AcceleratorsACM Computing Surveys10.1145/329104851:6(1-35)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3291048
Xiao JAndelfinger PEckhoff DCai WKnoll ARisco Martín JBesada EDe Rango F(2018)Exploring execution schemes for agent-based traffic simulation on heterogeneous hardwareProceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications10.5555/3330299.3330331(243-252)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.5555/3330299.3330331
Mewes BSchumann A(2018)IPA (v1): a framework for agent-based modelling of soil water movementGeoscientific Model Development10.5194/gmd-11-2175-201811:6(2175-2187)Online publication date: 13-Jun-2018
https://doi.org/10.5194/gmd-11-2175-2018
Show More Cited By

Index Terms

Accelerating simulation of agent-based models on heterogeneous architectures
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems

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cover image ACM Other conferences

GPGPU-6: Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units

March 2013

156 pages

ISBN:9781450320177

DOI:10.1145/2458523

Editors:
John Cavazos
University of Delaware
,
Xiang Gong,
David Kaeli

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 16 March 2013

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GPGPU-6

GPGPU-6: Sixth Workshop on General Purpose Processing Using GPUs

March 16, 2013

Texas, Houston, USA

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GPGPU-6 Paper Acceptance Rate 15 of 37 submissions, 41%;

Overall Acceptance Rate 57 of 129 submissions, 44%

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Cited By

Xiao JAndelfinger PEckhoff DCai WKnoll A(2019)A Survey on Agent-based Simulation Using Hardware AcceleratorsACM Computing Surveys10.1145/329104851:6(1-35)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3291048
Xiao JAndelfinger PEckhoff DCai WKnoll ARisco Martín JBesada EDe Rango F(2018)Exploring execution schemes for agent-based traffic simulation on heterogeneous hardwareProceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications10.5555/3330299.3330331(243-252)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.5555/3330299.3330331
Mewes BSchumann A(2018)IPA (v1): a framework for agent-based modelling of soil water movementGeoscientific Model Development10.5194/gmd-11-2175-201811:6(2175-2187)Online publication date: 13-Jun-2018
https://doi.org/10.5194/gmd-11-2175-2018
Xiao JAndelfinger PEckhoff DCai WKnoll A(2018)Exploring Execution Schemes for Agent-Based Traffic Simulation on Heterogeneous Hardware2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)10.1109/DISTRA.2018.8601016(1-10)Online publication date: Oct-2018
https://doi.org/10.1109/DISTRA.2018.8601016
Fachada NRosa AMcIntosh-Smith SBergen B(2017)Assessing the feasibility of OpenCL CPU implementations for agent-based simulationsProceedings of the 5th International Workshop on OpenCL10.1145/3078155.3078174(1-10)Online publication date: 16-May-2017
https://dl.acm.org/doi/10.1145/3078155.3078174
Fachada NLopes VMartins RRosa A(2017)Parallelization Strategies for Spatial Agent-Based ModelsInternational Journal of Parallel Programming10.1007/s10766-015-0399-945:3(449-481)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10766-015-0399-9
Liu WVinter BCavazos JGong XKaeli D(2014)ad-heapProceedings of Workshop on General Purpose Processing Using GPUs10.1145/2588768.2576786(54-63)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2588768.2576786
Ramapantulu LTudor BLoghin DVu TTeo Y(2014)Modeling the Energy Efficiency of Heterogeneous ClustersProceedings of the 2014 Brazilian Conference on Intelligent Systems10.1109/ICPP.2014.41(321-330)Online publication date: 18-Oct-2014
https://dl.acm.org/doi/10.1109/ICPP.2014.41

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